Sökning: "Multi-Objective Reinforcement Learning"

Visar resultat 1 - 5 av 8 uppsatser innehållade orden Multi-Objective Reinforcement Learning.

1. 1. Smart Tracking for Edge-assisted Object Detection : Deep Reinforcement Learning for Multi-objective Optimization of Tracking-based Detection Process

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Shihang Zhou; [2023]
   Nyckelord :Tracking-By-Detection; Deep Reinforcement Learning; Multi-Objective Optimization; Spårning genom detektion; Djup förstärkningsinlärning; Multiobjektiv optimering;

   Sammanfattning : Detecting generic objects is one important sensing task for applications that need to understand the environment, for example eXtended Reality (XR), drone navigation etc. However, Object Detection algorithms are particularly computationally heavy for real-time video analysis on resource-constrained mobile devices. LÄS MER

3. 2. Incorporating Reinforcement Learning into Supervised Sequential Recommender Models

   Master-uppsats, Linköpings universitet/Statistik och maskininlärning

   Författare :Patrick Siegfried Hiemsch; [2023]
   Nyckelord :;

   Sammanfattning : In the context of the significant expansion of e-commerce, Recommender Systems have become important tools for businesses, enhancing customer engagement through the personalization of product recommendations. This thesis investigates the integration of Reinforcement Learning concepts  into Supervised Learning frameworks, aiming to foster more accurate, novel and diverse recommendations. LÄS MER

4. 3. Reproducibility and Applicability of a Fuzzy-based Routing Algorithm in Wireless Sensor Networks

   Kandidat-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

   Författare :Hannes Rönningen; Erik Olofsson; [2023]
   Nyckelord :End-to-end delay; Energy-efficiency; Fuzzy logic; Lifetime maximization; Multi-hop network; Multiobjective optimization; Reinforcement learning; Routing; Routing-protocol; Shortest path; Wireless sensor networks; WSN localization;

   Sammanfattning : Wireless sensor networks is a broad subject with many applications and interesting research areas, such as optimization within connectivity and energy efficiency. One problem is that most published articles in this field use customized simulation environments and do not provide source code of their implementation. LÄS MER

5. 4. Access Point Selection and Clustering Methods with Minimal Switching for Green Cell-Free Massive MIMO Networks

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Qinglong He; [2022]
   Nyckelord :Cell-free massive MIMO; multi-objective optimization; deep reinforcement learning; AP switch ON OFF; energy efficiency; Cellfri massiv MIMO; multiobjektiv optimering; djup förstärkningsinlärning; AP switch ON OFF; energieffektivitet;

   Sammanfattning : As a novel beyond fifth-generation (5G) concept, cell-free massive MIMO (multiple-input multiple-output) recently has become a promising physical-layer technology where an enormous number of distributed access points (APs), coordinated by a central processing unit (CPU), cooperate to coherently serve a large number of user equipments (UEs) in the same time/frequency resource. However, denser AP deployment in cell-free networks as well as an exponentially growing number of mobile UEs lead to higher power consumption. LÄS MER

7. 5. Deep Reinforcement Learning and Simulation for the Optimization of Production Systems

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Siyuan Chen; [2022]
   Nyckelord :;

   Sammanfattning : The main objective of this master thesis project is to use the deep reinforcement learning (DRL) and simulation method for optimization of production systems. In this project, the Deep Q-learning Networks (DQN) algorithm is first used to optimize seven decision variables in Averill Law’s production system to find the best profit, with 99. LÄS MER